Conclusiones del Abogado General del Tribunal de Justicia de la Unión

Virtualisation technology appeared several years ago; it comes in many types, all focusing on control and usage schemes that emphasise efficiency. This efficiency is seen as a single terminal being able to run multiple machines or a single task run- ning over multiple computers via idle computing power. Adoption within data cen- tres and adoption by service providers is increasing rapidly and encompasses different proprietary virtualisation technologies. Again, the lack of standardisation poses a barrier to an open standards cloud that is interoperable with other clouds, and a broad array of computing and information resources is fundamentally imple- mentable. As the availability of requested resources by users poses a crucial param- eter for the adequacy of the service provided, one of the major deployments of the cloud application paradigm is the virtual data centres (VDC), utilised by service providers [21] by enabling a virtual infrastructure (Fig. 6.6) in a distributed manner in various remotely hosted locations worldwide to provide accessibility [27] and backup services and ensure reliability in case of a potential single site failure. In the case of resource saturation or resource dismissal, where a certain location-based resource cannot be accessed, the VDC claims the resource in order to enable avail- ability to potential requests/users. Additionally, these services with globally assigned operations require faster response time by distributing workload requests to multiple VCDs using certain scheduling and load-balancing methodologies. Therefore, as an optimal approach to resource availability, a k-rank model [45] can be applied in order to rank the requests and resources and create outsourcing ‘connectivity’ to potential request.

Currently, most computing servers in VDCs comprise of clusters of computers as they offer high performance, high availability of resources. Notwithstanding the high-throughput response is of vital essence, it should be offered at a lower cost compared to conventional high-performance computing (HPC) systems [28]. Resource manipulation in these clusters needs an adaptively dynamic decision mak- ing, in order to provide the service requirements’ response to users. By considering the dynamic amendments of the original service requests that should take place in order to avoid the resources’ deficiencies expressed by the traditional resource man- agement systems such as Condor [29], LoadLeveler [30], Portable Batch System and

Load Sharing Facility [28], someone needs to express the host-system parameters in the basis of resource-requests and the overall volume of needed resources. However, these types of systems adopt a system-centric resource allocation approaches that focus on optimising overall cluster performance in a selfish way.

To overcome the selfishness obstacle of VDC clusters, the utilised random segmentation policies of the requested resources need to be replaced by a new strat- egy which takes: (a) the semantic contents of the datasets, (b) the requirements of users/applications into account, i.e. data shall be distributed according to the interest in the data/information per time, and (c) the semantic resource applicability of the requested data for location-aware resource accessibility. For this reason, users, devices and applications need to be modelled by capturing relevant context parameters (e.g. the actual position and network properties) as well as analysing application states with respect to upcoming data retrieval and/or processing needs [31].

It is undoubtedly true that in a cloud environment, performance penalties may appear at two different levels: at the virtual engine (VE) level for which the perfor- mance is significantly reduced due to faulty or dismissed (non-updated) approach of the virtualisation mechanism and the overall cloud environment (CE) which causes losses to be introduced at a higher level by the cloud environment, and they are mainly due to overheads for the communication resources and due to the sharing or resource unavailability. The approach for facing this inefficiency is shown in Fig. 6.7, where the concept of virtual cluster is introduced which can execute

Fig. 6.6 VM workload processing (from service request monitor mechanism from different

multiple jobs in parallel, by assigning to every job a subset of the total number of CPUs. To exploit all the available computing resources and be able to fulfil requests, the application should use at least a number of processes equal to the number of available CPUs (or, in case of concurrent jobs, equal to the number of CPU exclu- sively reserved for the job [28]).

A virtual cluster is composed of a virtual front end and a number of virtual nodes (Fig. 6.7). Virtual front ends are obtained by virtualisation of a physical front-end machine, and virtual nodes are obtained by virtualisation of physical processing nodes. The advantages of cluster virtualisation are the simplicity that each applica- tion can set up a proper execution environment, which does not interfere with all other applications and virtual clusters running in the VLAN. Moreover, the volume of traffic in every virtual cluster is encapsulated in a separate VLAN of a different location. In this way, all VLANs will share the network resources in an efficient way by minimising the request/dismissal rate.

Given the scale at which physical resources exist in such environments, the like- lihood of having failures is relatively high rather than being an exception [20]. However, when problems such as power outage or network switch failures occur, a whole rack can fail, leading to multiple resources and servers being unavailable. Failures at such a scale are very likely to significantly reduce the efficiency, reli- ability offered to the end-recipient and consistency of the system. According to the literature, there are three possible ways of addressing the problem of multiple serves being unresponsive:

• Design dynamic resource allocation (DRA) policies that are failure-aware • Outsource the resources according to the forecasting and predictability policy

used by a service operator

• Execute a DRA policy when a failure is encountered

The issue of resource allocation is also addressed in research such as [20], where periodic balanced server provisioning was investigated. In contrast to experimental

Fig. 6.7 Bandwidth and access restrictions based on the SLA by service operator when accessing

research, analytical approaches to dynamic resource allocation have also been explored in work such as [32], [33] and [34], where application modelling and queuing theory were applied, respectively. Resource failures in the context of data centres are well addressed by existing literature [35, 36]. The coverage of resource failures has mostly concerned failures affecting a single hardware resource, exclud- ing the possibility of large-scale failures. The authors of [36] give an insight into the failure rates of servers in a large data centre and attempt to classify them using a range of criteria. The issue of resource failures in cloud computing is addressed in [35], where the authors develop a policy to partition a resource between high- performance computing application and web services where these resources can be accessed and found on a broker-based format. The algorithm proposed in [37] depicts that under a balanced resource allocator, which uses a parameterised fea- tures of the metrics that are affecting the resource-sharing process (e.g. the capacity loss metric as shown in Eq. 6.1), can effectively assign resources to users/devices, at the same time minimising the potential capacity loss in the event of unpredictable server failure occurs. The capacity loss metric is considered as follows:

Capacity Loss_{r a} i j a i j j s S , = (6.1)

where ri is the rack, aj is an application, sij is the number of servers in rack ri hosting

application aj and Saj is the number of servers hosting application across all racks. It

is used to capture the impact the failure of rack ri will have on the application aj in

terms of capacity. The above metric, as the performed estimations and experiments show, can effectively allocate requested resources to users, even if an unpredictable event occurs like a rack of massive server failures.