| 1. |
- Al-Dulaimy, Auday, et al.
(författare)
-
Privacy-Aware Job Submission in the Cloud
- 2019
-
Ingår i: 2019 2nd IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2019. - : IEEE. - 9781728136875
-
Konferensbidrag (refereegranskat)abstract
- The services offered by cloud computing are provided to individuals and organizations by varied shared resources which are forming the hardware layer of cloud data centers. Cloud users do not deal or interact directly with those resources, instead, they deal with the virtualized version of them, in other words, users deal with the virtualization layer which conceals to a great extent the specifics of the physical hardware layer. Based on the virtualization concept, more than one virtual machine can be co-hosted on the same physical machine. In spite of the wide range of benefits, co-hosting virtual machines on the same host comes with privacy and security threats. From one side, cloud providers are serving the virtual machines without being aware of their contents. On the other side, once cloud users submit their jobs to be serviced in the cloud, they lose their control on their jobs' sensitive information. Thus, cloud users' hesitation from moving to the cloud is logical since their sensitive jobs' content leakage or misuse is possible, especially when cloud services are not designed with privacy considerations. This paper proposes an approach to make the jobs with sensitive information more secure when submitted to the cloud environment. The core idea of the approach is to request the inclusion of the privacy specification of a set of one or more provider services in the Service Level Agreement contract.
|
|
| 2. |
- Alizadeh Noghani, Kyoomars, et al.
(författare)
-
On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration
- 2019
-
Konferensbidrag (refereegranskat)abstract
- Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and re-routing corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.
|
|
| 3. |
- Alizadeh Noghani, Kyoomars, et al.
(författare)
-
SDN helps volume in Big Data
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 185-206
-
Bokkapitel (refereegranskat)abstract
- Both Big Data and SDN are described in detail in previous chapters. This chapter investigates how SDN architecture can leverage its unique features to mitigate the challenges of Big Data volume. Accordingly, first, we provide an overview of Big Data volume, its effects on the underlying network, and mention some potential SDN solutions to address the corresponding challenges. Second, we elaborate more on the network-monitoring, traffic-engineering, and fault-tolerant mechanisms which we believe they may help to address the challenges of Big Data volume. Finally, this chapter is concluded with some open issues.
|
|
| 4. |
- Anwar, Adnan, et al.
(författare)
-
HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise
- 2017
-
Ingår i: IEEE Transactions on Smart Grid. - : IEEE. - 1949-3053 .- 1949-3061. ; 8:3, s. 1446-1459
-
Tidskriftsartikel (refereegranskat)abstract
- The performance of Volt/VAr optimization has been significantly improved due to the integration of measurement data obtained from the advanced metering infrastructure of a smart grid. However, most of the existing works lack: 1) realistic unbalanced multi-phase distribution system modeling; 2) scalability of the Volt/VAr algorithm for larger test system; and 3) ability to handle gross errors and noise in data processing. In this paper, we consider realistic distribution system models that include unbalanced loadings and multi-phased feeders and the presence of gross errors such as communication errors and device malfunction, as well as random noise. At the core of the optimization process is an intelligent particle swarm optimization-based technique that is parallelized using high performance computing technique to solve Volt/VAr-based power loss minimization problem. Extensive experiments covering the different aspects of the proposed framework show significant improvement over existing Volt/VAr approaches in terms of both the accuracy and scalability on IEEE 123 node and a larger IEEE 8500 node benchmark test systems.
|
|
| 5. |
- Bhamare, Deval, et al.
(författare)
-
IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring
- 2019
-
Ingår i: 2019 IEEE International Conference on Communications (ICC) Próceedings. - : IEEE. - 9781538680896 - 9781538680889
-
Konferensbidrag (refereegranskat)abstract
- Managing and scaling virtual network function(VNF) service chains require the collection and analysis ofnetwork statistics and states in real time. Existing networkfunction virtualization (NFV) monitoring frameworks either donot have the capabilities to express the range of telemetryitems needed to perform management or do not scale tolarge traffic volumes and rates. We present IntOpt, a scalableand expressive telemetry system designed for flexible VNFservice chain network monitoring using active probing. IntOptallows to specify monitoring requirements for individual servicechain, which are mapped to telemetry item collection jobsthat fetch the required telemetry items from P4 (programmingprotocol-independent packet processors) programmable dataplaneelements. In our approach, the SDN controller creates theminimal number of monitoring flows to monitor the deployedservice chains as per their telemetry demands in the network.We propose a simulated annealing based random greedy metaheuristic(SARG) to minimize the overhead due to activeprobing and collection of telemetry items. Using P4-FPGA, webenchmark the overhead for telemetry collection and compareour simulated annealing based approach with a na¨ıve approachwhile optimally deploying telemetry collection probes. Ournumerical evaluation shows that the proposed approach canreduce the monitoring overhead by 39% and the total delays by57%. Such optimization may as well enable existing expressivemonitoring frameworks to scale for larger real-time networks.
|
|
| 6. |
|
|
| 7. |
- Casas, Israel, et al.
(författare)
-
A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems
- 2017
-
Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 74, s. 168-178
-
Tidskriftsartikel (refereegranskat)abstract
- Cloud computing provides substantial opportunities to researchers who demand pay-as-you-go computing systems. Although cloud provider (e.g., Amazon Web Services) and application provider (e.g., biologists, physicists, and online gaming companies) both have specific performance requirements (e.g. application response time), it is the cloud scheduler’s responsibility to map the application to underlying cloud resources. This article presents a Balanced and file Reuse-Replication Scheduling (BaRRS) algorithm for cloud computing environments to optimally schedule scientific application workflows. BaRRS splits scientific workflows into multiple sub-workflows to balance system utilization via parallelization. It also exploits data reuse and replication techniques to optimize the amount of data that needs to be transferred among tasks at run-time. BaRRS analyzes the key application features (e.g., task execution times, dependency patterns and file sizes) of scientific workflows for adapting existing data reuse and replication techniques to cloud systems. Further, BaRRS performs a trade-off analysis to select the optimal solution based on two optimization constraints: execution time and monetary cost of running scientific workflows. BaRRS is compared with a state-of-the-art scheduling approach; experiments prove its superior performance. Experiments include four well known scientific workflows with different dependency patterns and data file sizes. Results were promising and also highlighted most critical factors affecting execution of scientific applications on clouds.
|
|
| 8. |
|
|
| 9. |
- Casas, Israel, et al.
(författare)
-
PSO-DS : a scheduling engine for scientific workflow managers
- 2017
-
Ingår i: Journal of Supercomputing. - : Springer Science and Business Media LLC. - 0920-8542 .- 1573-0484. ; 73:9, s. 3924-3947
-
Tidskriftsartikel (refereegranskat)abstract
- Cloud computing, an important source of computing power for the scientific community, requires enhanced tools for an efficient use of resources. Current solutions for workflows execution lack frameworks to deeply analyze applications and consider realistic execution times as well as computation costs. In this study, we propose cloud user-provider affiliation (CUPA) to guide workflow's owners in identifying the required tools to have his/her application running. Additionally, we develop PSO-DS, a specialized scheduling algorithm based on particle swarm optimization. CUPA encompasses the interaction of cloud resources, workflow manager system and scheduling algorithm. Its featured scheduler PSO-DS is capable of converging strategic tasks distribution among resources to efficiently optimize makespan and monetary cost. We compared PSO-DS performance against four well-known scientific workflow schedulers. In a test bed based on VMware vSphere, schedulers mapped five up-to-date benchmarks representing different scientific areas. PSO-DS proved its efficiency by reducing makespan and monetary cost of tested workflows by 75 and 78%, respectively, when compared with other algorithms. CUPA, with the featured PSO-DS, opens the path to develop a full system in which scientific cloud users can run their computationally expensive experiments.
|
|
| 10. |
- Cho, Daewoong, et al.
(författare)
-
Big Data helps SDN to optimize its controllers
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 389-408
-
Bokkapitel (refereegranskat)abstract
- In this chapter, we first discuss the basic features and recent issues of the SDN control plane, notably the controller element. Then, we present feasible ideas to address the SDN controller-related problems using Big Data analytics techniques. Accordingly, we propose that Big Data can help various aspects of the SDN controller to address scalability issue and resiliency problem. Furthermore, we proposed six applicable scenarios for optimizing the SDN controller using the Big Data analytics: (i) controller scale-up/out against network traffic concentration, (ii) controller scale-in for reduced energy usage, (iii) backup controller placement for fault tolerance and high availability, (iv) creating backup paths to improve fault tolerance, (v) controller placement for low latency between controllers and switches, and (vi) flow rule aggregation to reduce the SDN controller's traffic. Although real-world practices on optimizing SDN controllers using Big Data are absent in the literature, we expect scenarios we highlighted in this chapter to be highly applicable to optimize the SDN controller in the future.
|
|
