MidHDC: Advanced topics on middleware services for heterogeneous distributed computing. Part 2

Pop, Florin; Zhu, Xiaomin; Yang, Laurence T.

doi:10.1016/j.future.2017.05.010

Cited by 4 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The special issues follows the previous published work like ARMCO (advanced topics in resource management for ubiquitous cloud computing: an adaptive approach) [1], and MidHDC (advanced topics on middleware services for heterogeneous distributed computing) part 1 and part 2 [2,3].…”

Section: This Special Issuementioning

confidence: 99%

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

Pop

Iosup

Prodan

2018

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, dataaware service allocation, Big Data management and processing, performance analysis and optimization.

show abstract

Section: This Special Issuementioning

confidence: 99%

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

Pop

Iosup

Prodan

2018

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

show abstract

AutoCompBD: Autonomic Computing and Big Data platforms

2017

Self Cite

View full text Add to dashboard Cite

The amount of data collected or generated by ICT systems is growing exponentially (today we reached a Petabyte Era and will soon enter the ExaScale one). Processing and storing ever-larger volumes of data introduces new challenges, and consequently, we need to constantly develop new technological means to face them. Massive parallel processing platforms are the answer and are already being developed over distributed systems (i.e., over cloud or fog computing). However, the problem is that such platforms need to support a wide variety of applications, coming with different processing requirements. Thus, self-* behavior is a must in this context, referring to self-managing characteristics of distributed computing resources, their capability to adapt to unpredictable changes while hiding intrinsic complexity to operators and users. This special issue is dedicated to dissemination and evaluation of advances in Autonomic Computing and Big Data platforms, supported by largescale distributed systems (LSDS). Autonomic Computing is facilitated by self-management capabilities that modern LSDS introduce, such as self-configuration, self-healing, self-optimization, and self-protection properties. In LSDS, an important characteristic is dependability (defined in terms B Florin Pop

show abstract