82 research outputs found
A Latency-driven Availability Assessment for Multi-Tenant Service Chains
Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests
A survey on elasticity management in PaaS systems
[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. Because of this, there are still several aspects that deserve additional research for finding optimal adaptability strategies. Those open issues are also discussed.This work has been partially supported by EU FEDER and Spanish MINECO under research Grant TIN2012-37719-C03-01.Muñoz-EscoĂ, FD.; Bernabeu AubĂĄn, JM. (2017). A survey on elasticity management in PaaS systems. Computing. 99(7):617-656. https://doi.org/10.1007/s00607-016-0507-8S617656997Ajmani S (2004) Automatic software upgrades for distributed systems. PhD thesis, Department of Electrical and Computer Science, Massachusetts Institute of Technology, USAAjmani S, Liskov B, Shrira L (2006) Modular software upgrades for distributed systems. In: 20th European Conference on Object-Oriented Programming (ECOOP), Nantes, France, pp 452â476Alhamad M, Dillon TS, Chang E (2010) Conceptual SLA framework for cloud computing. 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A survey on fault-models for QoS studies of service-oriented systems
This survey paper presents an overview of the fault-models available to the
researcher who wants to parameterise system-models in order to study Quality-
of-Service (QoS) properties of systems with service-oriented architecture. The
concept of a system-model subsumes the whole spectrum between abstract
mathematical models and testbeds based on actual implementations. Fault-
models, on the other hand, are parameters to system-models. They introduce
faults and disturbances into the system-model, thereby allowing the study of
QoS under realistic conditions. In addition to a survey of existing fault-
models, the paper also provides a discussion of available fault-classification
schemes
Mathematics in Software Reliability and Quality Assurance
This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment
Workload Prediction for Efficient Performance Isolation and System Reliability
In large-scaled and distributed systems, like multi-tier storage systems and cloud data centers, resource sharing among workloads brings multiple benefits while introducing many performance challenges. The key to effective workload multiplexing is accurate workload prediction. This thesis focuses on how to capture the salient characteristics of the real-world workloads to develop workload prediction methods and to drive scheduling and resource allocation policies, in order to achieve efficient and in-time resource isolation among applications. For a multi-tier storage system, high-priority user work is often multiplexed with low-priority background work. This brings the challenge of how to strike a balance between maintaining the user performance and maximizing the amount of finished background work. In this thesis, we propose two resource isolation policies based on different workload prediction methods: one is a Markovian model-based and the other is a neural networks-based. These policies aim at, via workload prediction, discovering the opportune time to schedule background work with minimum impact on user performance. Trace-driven simulations verify the efficiency of the two pro- posed resource isolation policies. The Markovian model-based policy successfully schedules the background work at the appropriate periods with small impact on the user performance. The neural networks-based policy adaptively schedules user and background work, resulting in meeting both performance requirements consistently. This thesis also proposes an accurate while efficient neural networks-based pre- diction method for data center usage series, called PRACTISE. Different from the traditional neural networks for time series prediction, PRACTISE selects the most informative features from the past observations of the time series itself. Testing on a large set of usage series in production data centers illustrates the accuracy (e.g., prediction error) and efficiency (e.g., time cost) of PRACTISE. The superiority of the usage prediction also allows a proactive resource management in the highly virtualized cloud data centers. In this thesis, we analyze on the performance tickets in the cloud data centers, and propose an active sizing algorithm, named ATM, that predicts the usage workloads and re-allocates capacity to work- loads to avoid VM performance tickets. Moreover, driven by cheap prediction of usage tails, we also present TailGuard in this thesis, which dynamically clones VMs among co-located boxes, in order to efficiently reduce the performance violations of physical boxes in cloud data centers
Recommended from our members
Modelling and Quantitative Analysis of Performance vs Security Trade-offs in Computer Networks: An investigation into the modelling and discrete-event simulation analysis of performance vs security trade-offs in computer networks, based on combined metrics and stochastic activity networks (SANs)
Performance modelling and evaluation has long been considered of paramount
importance to computer networks from design through development, tuning and
upgrading. These networks, however, have evolved significantly since their first introduction
a few decades ago. The Ubiquitous Web in particular with fast-emerging
unprecedented services has become an integral part of everyday life. However, this
all is coming at the cost of substantially increased security risks. Hence cybercrime is
now a pervasive threat for todayâs internet-dependent societies. Given the frequency
and variety of attacks as well as the threat of new, more sophisticated and destructive
future attacks, security has become more prevalent and mounting concern in
the design and management of computer networks. Therefore equally important if
not more so is security.
Unfortunately, there is no one-size-fits-all solution to security challenges. One security
defence system can only help to battle against a certain class of security threats. For overall security, a holistic approach including both reactive and proactive
security measures is commonly suggested. As such, network security may have
to combine multiple layers of defence at the edge and in the network and in its
constituent individual nodes.
Performance and security, however, are inextricably intertwined as security measures
require considerable amounts of computational resources to execute. Moreover, in
the absence of appropriate security measures, frequent security failures are likely
to occur, which may catastrophically affect network performance, not to mention
serious data breaches among many other security related risks.
In this thesis, we study optimisation problems for the trade-offs between performance
and security as they exist between performance and dependability. While
performance metrics are widely studied and well-established, those of security are
rarely defined in a strict mathematical sense. We therefore aim to conceptualise and
formulate security by analogy with dependability so that, like performance, it can
be modelled and quantified.
Having employed a stochastic modelling formalism, we propose a new model for a
single node of a generic computer network that is subject to various security threats.
We believe this nodal model captures both performance and security aspects of a
computer node more realistically, in particular the intertwinements between them.
We adopt a simulation-based modelling approach in order to identify, on the basis
of combined metrics, optimal trade-offs between performance and security and facilitate
more sophisticated trade-off optimisation studies in the field.
We realise that system parameters can be found that optimise these abstract combined
metrics, while they are optimal neither for performance nor for security individually.
Based on the proposed simulation modelling framework, credible numerical
experiments are carried out, indicating the scope for further work extensions for a
systematic performance vs security tuning of computer networks
A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing
With the rapid growth of Internet of Things (IoT), cloud-centric application management raises
questions related to quality of service for real-time applications. Fog and edge computing
(FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource
management on multiple resources from distributed and administrative FEC nodes is a key
challenge to ensure the quality of end-userâs experience. To improve resource utilisation and
system performance, researchers have been proposed many fair allocation mechanisms for
resource management. Dominant Resource Fairness (DRF), a resource allocation policy for
multiple resource types, meets most of the required fair allocation characteristics. However,
DRF is suitable for centralised resource allocation without considering the effects (or
feedbacks) of large-scale distributed environments like multi-controller software defined
networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium
equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to
âproportionatelyâ share resources among distributed participants. Although CEEIâs
decentralised policy guarantees load balancing for performance isolation, they are not faultproof
for computation offloading.
The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of
decentralised SDN controller deployment. We apply multi-agent reinforcement learning
(MARL) with robustness against adversarial controllers to enable efficient priority scheduling
for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by
applying the principles of feedback (positive or/and negative network effects) in reverse game
theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask
offloading/forwarding in FEC environments.
In the first piece of study, monotonic scheduling for joint offloading at the federated edge is
addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and
positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL
approach applying partition form game (PFG) to guarantee second-best Pareto optimality
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(SBPO) in allocation of multi-resources from deterministic policy in both population and
resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to
address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical
networks by applying constrained coalition formation (CCF) games to implement MARL. The
multi-objective optimisation problem for fog throughput maximisation is solved via a
constraint dimensionality reduction methodology using fairness constraints for efficient
gateway and low-level controllerâs placement.
For evaluation, we develop an agent-based framework to implement fair allocation policies in
distributed data centre environments. In empirical results, the deterministic policy of IP-DRF
scheme provides SBPO and reduces the average execution and turnaround time by 19% and
11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets
in population non-monotonic settings. The processing cost of tasks shows significant
improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic
setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair
(MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC
nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the
efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and
network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria
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