Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Cloud computing : how does it differ from IT outsourcing and what are the implications for practice and research?

The purpose of this paper is to provide an evolutionary perspective of cloud computing (CC) by integrating two previously disparate literatures: CC and information technology outsourcing (ITO). We review the literature and develop a framework that highlights the demand for the CC service, benefits, risks, as well as risk mitigation strategies that are likely to influence the success of the service. CC success in organisations and as a technology overall is a function of (i) the outsourcing decision and supplier selection, (ii) contractual and relational governance, and (iii) industry standards and legal framework. Whereas CC clients have little control over standards and/or the legal framework, they are able to influence other factors to maximize the benefits while limiting the risks. This paper provides guidelines for (potential) cloud computing users with respect to the outsourcing decision, vendor selection, service-level-agreements, and other issues that need to be addressed when opting for CC services. We contribute to the literature by providing an evolutionary and holistic view of CC that draws on the extensive literature and theory of ITO. We conclude the paper with a number of research paths that future researchers can follow to advance the knowledge in this field.

Outsourcing and Subcontracting

Outsourcing, or contracting-out as it is also known, is a prevalent business practice across all sectors of the economy. This entry will give a number of explanations about why organizations outsource, as well as a number of difficulties which may be encountered when outsourcing...

Intra-organizational information asymmetry in offshore ISD outsourcing

Purpose - Contemporary offshore Information System Development (ISD) outsourcing is becoming even more complex. Outsourcing partner has begun ‘re-outsourcing’ components of their projects to other outsourcing companies to minimize cost and gain efficiencies. This paper aims to explore intra-organizational Information Asymmetry of re-outsourced offshore ISD outsourcing projects. Design/methodology/approach - An online survey was conducted to get an overall view of Information Asymmetry between Principal and Agents (as per the Agency theory). Findings - Statistical analysis showed that there are significant differences between the Principal and Agent on clarity of requirements, common domain knowledge and communication effectiveness constructs, implying an unbalanced relationship between the parties. Moreover, our results showed that these three are significant measurement constructs of Information Asymmetry. Research limitations/implications - In our study we have only considered three main factors as common domain knowledge, clarity of requirements and communication effectiveness as three measurement constructs of Information Asymmetry. Therefore, researches are encouraged to test the proposed constructs further to increase its precision. Practical implications - Our analysis indicates significant differences in all three measurement constructs, implying the difficulties to ensure that the Agent is performing according to the requirements of the Principal. Using the Agency theory as theoretical view, this study sheds light on the best contract governing methods which minimize Information Asymmetry between the multiple partners within ISD outsourcing organizations. Originality/value - Currently, to the best of our knowledge, no study has undertaken research on Intra-organizational Information Asymmetry in re-outsourced offshore ISD outsourcing projects.

Information systems development outsourcing : the role of control configurations

The Control Theory has provided a useful theoretical foundation for Information Systems development outsourcing (ISD-outsourcing) to examine the co-ordination between the client and the vendor. Recent research identified two control mechanisms: structural (structure of the control mode) and process (the process through which the control mode is enacted). Yet, the Control Theory research to-date does not describe the ways in which the two control mechanisms can be combined to ensure project success. Grounded in case study data of eight ISD-outsourcing projects, we derive three ‘control configurations’; i) aligned, ii) negotiated, and 3) self-managed, which describe the combinative patterns of structural and process control mechanisms within and across control modes.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Approximate Bayesian computation using auxiliary model based estimates

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

Graph coloring applied to secure computation in non-Abelian groups

We study the natural problem of secure n-party computation (in the computationally unbounded attack model) of circuits over an arbitrary finite non-Abelian group (G,⋅), which we call G-circuits. Besides its intrinsic interest, this problem is also motivating by a completeness result of Barrington, stating that such protocols can be applied for general secure computation of arbitrary functions. For flexibility, we are interested in protocols which only require black-box access to the group G (i.e. the only computations performed by players in the protocol are a group operation, a group inverse, or sampling a uniformly random group element). Our investigations focus on the passive adversarial model, where up to t of the n participating parties are corrupted.

Multi-party computation with conversion of secret sharing

Classical results in unconditionally secure multi-party computation (MPC) protocols with a passive adversary indicate that every n-variate function can be computed by n participants, such that no set of size t < n/2 participants learns any additional information other than what they could derive from their private inputs and the output of the protocol. We study unconditionally secure MPC protocols in the presence of a passive adversary in the trusted setup (‘semi-ideal’) model, in which the participants are supplied with some auxiliary information (which is random and independent from the participant inputs) ahead of the protocol execution (such information can be purchased as a “commodity” well before a run of the protocol). We present a new MPC protocol in the trusted setup model, which allows the adversary to corrupt an arbitrary number t < n of participants. Our protocol makes use of a novel subprotocol for converting an additive secret sharing over a field to a multiplicative secret sharing, and can be used to securely evaluate any n-variate polynomial G over a field F, with inputs restricted to non-zero elements of F. The communication complexity of our protocol is O(ℓ · n 2) field elements, where ℓ is the number of non-linear monomials in G. Previous protocols in the trusted setup model require communication proportional to the number of multiplications in an arithmetic circuit for G; thus, our protocol may offer savings over previous protocols for functions with a small number of monomials but a large number of multiplications.

Active security in multiparty computation over black-box groups

Most previous work on unconditionally secure multiparty computation has focused on computing over a finite field (or ring). Multiparty computation over other algebraic structures has not received much attention, but is an interesting topic whose study may provide new and improved tools for certain applications. At CRYPTO 2007, Desmedt et al introduced a construction for a passive-secure multiparty multiplication protocol for black-box groups, reducing it to a certain graph coloring problem, leaving as an open problem to achieve security against active attacks. We present the first n-party protocol for unconditionally secure multiparty computation over a black-box group which is secure under an active attack model, tolerating any adversary structure Δ satisfying the Q 3 property (in which no union of three subsets from Δ covers the whole player set), which is known to be necessary for achieving security in the active setting. Our protocol uses Maurer’s Verifiable Secret Sharing (VSS) but preserves the essential simplicity of the graph-based approach of Desmedt et al, which avoids each shareholder having to rerun the full VSS protocol after each local computation. A corollary of our result is a new active-secure protocol for general multiparty computation of an arbitrary Boolean circuit.

Multi-party computation with omnipresent adversary

Secure multi-party computation (MPC) protocols enable a set of n mutually distrusting participants P 1, ..., P n , each with their own private input x i , to compute a function Y = F(x 1, ..., x n ), such that at the end of the protocol, all participants learn the correct value of Y, while secrecy of the private inputs is maintained. Classical results in the unconditionally secure MPC indicate that in the presence of an active adversary, every function can be computed if and only if the number of corrupted participants, t a , is smaller than n/3. Relaxing the requirement of perfect secrecy and utilizing broadcast channels, one can improve this bound to t a  < n/2. All existing MPC protocols assume that uncorrupted participants are truly honest, i.e., they are not even curious in learning other participant secret inputs. Based on this assumption, some MPC protocols are designed in such a way that after elimination of all misbehaving participants, the remaining ones learn all information in the system. This is not consistent with maintaining privacy of the participant inputs. Furthermore, an improvement of the classical results given by Fitzi, Hirt, and Maurer indicates that in addition to t a actively corrupted participants, the adversary may simultaneously corrupt some participants passively. This is in contrast to the assumption that participants who are not corrupted by an active adversary are truly honest. This paper examines the privacy of MPC protocols, and introduces the notion of an omnipresent adversary, which cannot be eliminated from the protocol. The omnipresent adversary can be either a passive, an active or a mixed one. We assume that up to a minority of participants who are not corrupted by an active adversary can be corrupted passively, with the restriction that at any time, the number of corrupted participants does not exceed a predetermined threshold. We will also show that the existence of a t-resilient protocol for a group of n participants, implies the existence of a t’-private protocol for a group of n′ participants. That is, the elimination of misbehaving participants from a t-resilient protocol leads to the decomposition of the protocol. Our adversary model stipulates that a MPC protocol never operates with a set of truly honest participants (which is a more realistic scenario). Therefore, privacy of all participants who properly follow the protocol will be maintained. We present a novel disqualification protocol to avoid a loss of privacy of participants who properly follow the protocol.

Secure computation of the vector dominance problem

Suppose two parties, holding vectors A = (a 1,a 2,...,a n ) and B = (b 1,b 2,...,b n ) respectively, wish to know whether a i  > b i for all i, without disclosing any private input. This problem is called the vector dominance problem, and is closely related to the well-studied problem for securely comparing two numbers (Yao’s millionaires problem). In this paper, we propose several protocols for this problem, which improve upon existing protocols on round complexity or communication/computation complexity.

Generalized aggregate Quality of Service computation for composite services

This article addresses the problem of estimating the Quality of Service (QoS) of a composite service given the QoS of the services participating in the composition. Previous solutions to this problem impose restrictions on the topology of the orchestration models, limiting their applicability to well-structured orchestration models for example. This article lifts these restrictions by proposing a method for aggregate QoS computation that deals with more general types of unstructured orchestration models. The applicability and scalability of the proposed method are validated using a collection of models from industrial practice.

Aggregate quality of service computation for composite services

This paper addresses the problem of computing the aggregate QoS of a composite service given the QoS of the services participating in the composition. Previous solutions to this problem are restricted to composite services with well-structured orchestration models. Yet, in existing languages such as WS-BPEL and BPMN, orchestration models may be unstructured. This paper lifts this limitation by providing equations to compute the aggregate QoS for general types of irreducible unstructured regions in orchestration models. In conjunction with existing algorithms for decomposing business process models into single-entry-single-exit regions, these functions allow us to cover a larger set of orchestration models than existing QoS aggregation techniques.

Simplified computation and generalization of the refined process structure tree

A business process is often modeled using some kind of a directed flow graph, which we call a workflow graph. The Refined Process Structure Tree (RPST) is a technique for workflow graph parsing, i.e., for discovering the structure of a workflow graph, which has various applications. In this paper, we provide two improvements to the RPST. First, we propose an alternative way to compute the RPST that is simpler than the one developed originally. In particular, the computation reduces to constructing the tree of the triconnected components of a workflow graph in the special case when every node has at most one incoming or at most one outgoing edge. Such graphs occur frequently in applications. Secondly, we extend the applicability of the RPST. Originally, the RPST was applicable only to graphs with a single source and single sink such that the completed version of the graph is biconnected. We lift both restrictions. Therefore, the RPST is then applicable to arbitrary directed graphs such that every node is on a path from some source to some sink. This includes graphs with multiple sources and/or sinks and disconnected graphs.