Dr Satish Kumar

The rapidly changing workload of service-based systems can easily cause under-/over-utilization on the component services, which can consequently affect the overall Quality of Service (QoS), such as latency. Self-adaptive services composition rectifies this problem, but poses several challenges: (i) the effectiveness of adaptation can deteriorate due to over-optimistic assumptions on the latency and utilization constraints, at both local and global levels; and (ii) the benefits brought by each composition plan is often short term and is not often designed for long-term benefits---a natural prerequisite for sustaining the system. To tackle these issues, we propose a two levels constraint reasoning framework for sustainable self-adaptive services composition, called DATESSO. In particular, DATESSO consists of a refined formulation that differentiates the `strictness' for latency/utilization constraints in two levels. To strive for long-term benefits, DATESSO leverages the concept of technical debt and time-series prediction to model the utility contribution of the component services in the composition. The approach embeds a debt-aware two level constraint reasoning algorithm in DATESSO to improve the efficiency, effectiveness and sustainability of self-adaptive service composition. We evaluate DATESSO on a service-based system with real-world WS-DREAM dataset and comparing it with other state-of-the-art approaches. The results demonstrate the superiority of DATESSO over the others on the utilization, latency and running time whilst likely to be more sustainable.

In Software as a Service (SaaS)cloud marketplace, several functionally equivalent services tend to be available with different Quality of Service (QoS)values. For processing end-users multi-dimensional QoS and functional requirements, the application engineers are required to choose suitable services and optimize the service composition plans for each category of users. However, existing approaches for dynamic services composition tend to support execution plans that search for service provisions of equivalent functionalities with varying QoS or cost constraints to meet the tenants' QoS requirements or to dynamically respond to changes in QoS. These approaches tend to ignore the fact that multi-tenant execution plans need to provide variant execution plans, each offering a customized plan for a given tenant with its functionality, QoS and cost requirements. Henceforth, the dynamic selection and composition of multi-tenant service composition is a NP-hard dynamic multiobjective optimization problem. To address these challenges, we propose a novel multi-tenant middleware for dynamic service composition in the SaaS cloud. In particular, we present new encoding representation and fitness functions that model the service selection and composition as an evolutionary search. We incorporate our approach with two Multi-Objective Evolutionary Algorithms (MOEA), i.e., MOEA/D-STM and NSGA-II, to perform a comparative study. The experiment results show that the MOEA/D-STM outperforms NSGA-II in terms of quality of solutions and computation time.

The software project scheduling (SPS) is a project-scheduling problem where limited human resources are assigned to the tasks in multi-team project settings. Besides other dynamic events, employees experience evolution has direct influence in completing large-scale projects within budget and time. In this paper, a new SPS model is developed as a dynamic multi-objective optimization problem, which incorporates employees experience evolution with their learning ability over time. The experimental results on 24 problem instances (including six real-world instances) show that the developed SPS model reduces project duration by 40% while being within budget. The results provide evidence that consideration of experience evolution while tasks reallocation under dynamic events significantly optimizes project schedules. Moreover, the developed SPS model is evaluated with six state-of-the-art algorithms as bi-criterion evolution (BCE), NSGA-II, NSGA-III, Two_Arch2, OMOPSO, speed-constrained multi-objective particle swarm optimization (SMPSO) where BCE demonstrated distinct superiority for 63% data instances.

A composite service in multi-tenant SaaS cloud would inevitably operate under dynamic changes on the workload from the tenants, and thus it is not uncommon for the composition to encounter under-utilization and over-utilization on the component services. However, both of those cases could be good or bad: the former implies that although there is under-utilization, the pay-off afterwards are more significant; the latter, in contrast, refers to the over-utilization that leads to trivial pay-off, or nothing at all. Such a notion perfectly matches with the Technical Debt (TD)metaphor in Software Engineering. As a result, it is necessary to identify the root causes of the debts and where the debt can be manifested in the service composition, which, in turn, would offer great helps on the decision making process of service composition. In this paper, we propose a novel approach for identifying the technical debt in service composition under SaaS cloud. The approach combines time series forecasting and a newly proposed technical debt model to estimate the future debt and utility in the service composition. Through a real world case study, we demonstrate that our approach can successfully identify both the good and bad debts, while producing satisfactory accuracy on estimating the technical debt in the service composition under SaaS cloud.

Given the changing workloads from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilization and over-utilization on the component services. Both cases are undesirable and it is therefore nature to mitigate them by recomposing the services to a newly optimized composition plan once they have been detected. However, this ignores the fact that under-/over-utilization can be merely caused by temporary effects, and thus the advantages may be short-term, which hinders the long-term benefits that could have been created by the original composition plan, while generating unnecessary overhead and disturbance via recomposition. In this article, we propose DebtCom , a framework that determines whether to trigger recomposition based on the technical debt metaphor and time-series prediction of workload. In particular, we propose a service debt model, which has been explicitly designed for the context of service composition, to quantify the debt. Our core idea is that recomposition can be unnecessary if the under-/over-utilization only cause temporarily negative effects, and the current composition plan, although carries debt, can generate greater benefit in the long-term. We evaluate DebtCom on a large scale service system with up to 10 abstract services, each of which has 100 component services, under real-world dataset and workload traces. The results confirm that, in contrast to the state-of-the-art, DebtCom achieves better utility while having lower cost and number of recompositions, rendering each composition plan more sustainable.

Resource provisioning plays a pivotal role in determining the right amount of infrastructure resource to run applications and reduce the monetary cost. A significant portion of production clusters is now dedicated to long-running applications (LRAs), which are typically in the form of microservices and executed in the order of hours or even months. It is therefore practically important to plan ahead the placement of LRAs in a shared cluster for the minimized number of compute nodes required by them. Existing works on LRA scheduling are often application-agnostic, without particularly addressing the constraining requirements imposed by LRAs, such as co-location affinity constraints and time-varying resource requirements. In this paper, we present an affinity-aware resource provisioning approach for deploying large-scale LRAs in a shared cluster subject to multiple constraints, with the objective of minimizing the number of compute nodes in use. We investigate a broad range of solution algorithms which fall into three main categories: Application-Centric, Node-Centric, and Multi-Node approaches, and tune them for typical large-scale real-world scenarios. Experimental studies driven by the Alibaba Tianchi dataset show that our algorithms can achieve competitive scheduling effectiveness and running time, as compared with the heuristics used by the latest work including Medea and LraSched. Best results are obtained by the Application-Centric algorithms, if the algorithm's running time is of primary concern, and by Multi-Node algorithms, if the solution quality is of primary concern.

User preferences on throughput, latency, cost, service location, etc. indicate specific requirements when choosing a web service from the cloud marketplace. Service providers can also adopt preferences to prioritize a set of end-users based on their Service Level Agreement and service usage history. An effective matching between preferences from both parties enables fair service marketing in the cloud marketplace. The existing approaches are insufficient in capturing both parties’ preferences in the service composition process. To address this limitation, we propose MatchCom, a novel service composition approach driven by diverse preferences and formulate it as the stable marriage problem. Particularly, we present a novel fair preference ordering mechanism – in the context of a cloud marketplace, for enabling users to specify services provider ranking based on the capability they can provision, and for helping providers select the most suitable users to be served given users’ profile. MatchCom extends the Gale-Shapely Algorithm with a service composer algorithm for optimising the stable service composition. We evaluate MatchCom on a service-oriented system with 10 abstract services, each of which has 100 candidate web services. We establish through the experimental results that MatchCom outperforms other baseline approaches and can maximize end-user satisfaction in the composition process.

Microservices and Kubernetes are increasingly adopted for building and deploying large-scale distributed software systems in cloud computing environment. A microservice architecture divides an application into smaller, loosely coupled microservices, each of which can be deployed and scaled independently in Kubernetes cluster. While this flexibility allows for dynamic scaling of microservice instances to meet user demands, but the complex dependencies among these microservices can pose challenges in effectively managing microservices application performance and their impact on the Quality of Service (QoS) during on-demand instances scheduling. In this paper, we propose LOCUS, a locality aware QoS optimizer for scheduling microservices in Kubernetes cluster. The locus-optimizer is designed on top of observability ecosystem that leverage real-time performance metrics data to optimize node selection process in default Kubernetes scheduling framework. Further, the locality awareness implicitly favors the microservices dependencies without creating hard-rules based scheduling process. We evaluate our approach on a large scale microservices workload. The results confirm that in contrast to the default scheduling mechanism, the locus-optimizer achieves better microservices QoS.

Kubernetes based microservices deployments are increasingly adopted in modern cloud computing environments. However, securing microservices in Kubernetes cluster is critical because they operate in distributed manner across different nodes and each of which gives a potential entry point for attackers due to weak authentication mechanism or misconfiguration of security policies. To address these issues, we propose two layered policy-driven security mechanism for securing microservices workflow in Kubernetes cluster. The first layer employs mutual TLS (mTLS) for secure service-To-service authentication using the Istio service mesh. The second layer introduces workflow-based authorization policy enforced through Open Policy Agent (OPA) Gatekeeper. Experimental results demonstrate that unauthorized access is effectively blocked at the entry point, confirming that the proposed approach establishes a robust, multi-layered security environment for Kubernetes-based microservices.

In order to accommodate latency-sensitive IoT and AI workloads, serverless computing is becoming more popular in edge environment. However, the default Kubernetes scheduler ignores the energy and performance limitations of edge nodes and is resource-agnostic. Prior approaches usually only optimized for latency or energy, ignoring the combined effects of cold-start dynamics, inter-node communication, and inter-service dependencies. In this work, we propose a lightweight heuristic scheduling approach that combines inter-service traffic, energy, and latency into a single cost function. This approach, implemented as a custom Kubernetes Scheduling Framework plugin, has low overhead and is used in conjunction with a descheduler that consolidates workloads by draining underutilized nodes. Short-term responsive placements and long-term energy efficiency are made possible by this combination. We test the system on a Raspberry Pi cluster, using Knative workloads that are typical of IoT analytics workflows. The average latency decreased by 29%, failure rates decreased by 74%, and energy consumption per request reduced by 32%, all of which are consistent improvements over the default scheduler. These results show that multi-objective, metrics-aware placement can significantly improve serverless edge platforms’ quality of service objectives and energy efficiency, specifically when combined with descheduling for consolidation.